Information technologies for pain management

Millions of people around the world suffer from pain, acute or chronic and this raises the importance of its screening, assessment and treatment. The importance of pain is attested by the fact that it is considered the fifth vital sign for indicating basic bodily functions, health and quality of life, together with the four other vital signs: blood pressure, body temperature, pulse rate and respiratory rate. However, while these four signals represent an objective physical parameter, the occurrence of pain expresses an emotional status that happens inside the mind of each individual and therefore, is highly subjective that makes difficult its management and evaluation. For this reason, the self-report of pain is considered the most accurate pain assessment method wherein patients should be asked to periodically rate their pain severity and related symptoms. Thus, in the last years computerised systems based on mobile and web technologies are becoming increasingly used to enable patients to report their pain which lead to the development of electronic pain diaries (ED). This approach may provide to health care professionals (HCP) and patients the ability to interact with the system anywhere and at anytime thoroughly changes the coordinates of time and place and offers invaluable opportunities to the healthcare delivery. However, most of these systems were designed to interact directly to patients without presence of a healthcare professional or without evidence of reliability and accuracy. In fact, the observation of the existing systems revealed lack of integration with mobile devices, limited use of web-based interfaces and reduced interaction with patients in terms of obtaining and viewing information. In addition, the reliability and accuracy of computerised systems for pain management are rarely proved or their effects on HCP and patients outcomes remain understudied. This thesis is focused on technology for pain management and aims to propose a monitoring system which includes ubiquitous interfaces specifically oriented to either patients or HCP using mobile devices and Internet so as to allow decisions based on the knowledge obtained from the analysis of the collected data. With the interoperability and cloud computing technologies in mind this system uses web services (WS) to manage data which are stored in a Personal Health Record (PHR). A Randomised Controlled Trial (RCT) was implemented so as to determine the effectiveness of the proposed computerised monitoring system. The six weeks RCT evidenced the advantages provided by the ubiquitous access to HCP and patients so as to they were able to interact with the system anywhere and at anytime using WS to send and receive data. In addition, the collected data were stored in a PHR which offers integrity and security as well as permanent on line accessibility to both patients and HCP. The study evidenced not only that the majority of participants recommend the system, but also that they recognize it suitability for pain management without the requirement of advanced skills or experienced users. Furthermore, the system enabled the definition and management of patient-oriented treatments with reduced therapist time. The study also revealed that the guidance of HCP at the beginning of the monitoring is crucial to patients' satisfaction and experience stemming from the usage of the system as evidenced by the high correlation between the recommendation of the application, and it suitability to improve pain management and to provide medical information. There were no significant differences regarding to improvements in the quality of pain treatment between intervention group and control group. Based on the data collected during the RCT a clinical decision support system (CDSS) was developed so as to offer capabilities of tailored alarms, reports, and clinical guidance. This CDSS, called Patient Oriented Method of Pain Evaluation System (POMPES), is based on the combination of several statistical models (one-way ANOVA, Kruskal-Wallis and Tukey-Kramer) with an imputation model based on linear regression. This system resulted in fully accuracy related to decisions suggested by the system compared with the medical diagnosis, and therefore, revealed it suitability to manage the pain. At last, based on the aerospace systems capability to deal with different complex data sources with varied complexities and accuracies, an innovative model was proposed. This model is characterized by a qualitative analysis stemming from the data fusion method combined with a quantitative model based on the comparison of the standard deviation together with the values of mathematical expectations. This model aimed to compare the effects of technological and pen-and-paper systems when applied to different dimension of pain, such as: pain intensity, anxiety, catastrophizing, depression, disability and interference. It was observed that pen-and-paper and technology produced equivalent effects in anxiety, depression, interference and pain intensity. On the contrary, technology evidenced favourable effects in terms of catastrophizing and disability. The proposed method revealed to be suitable, intelligible, easy to implement and low time and resources consuming. Further work is needed to evaluate the proposed system to follow up participants for longer periods of time which includes a complementary RCT encompassing patients with chronic pain symptoms. Finally, additional studies should be addressed to determine the economic effects not only to patients but also to the healthcare system

From 'tree' based Bayesian networks to mutual information classifiers : deriving a singly connected network classifier using an information theory based technique

For reasoning under uncertainty the Bayesian network has become the representation of choice. However, except where models are considered 'simple' the task of construction and inference are provably NP-hard. For modelling larger 'real' world problems this computational complexity has been addressed by methods that approximate the model. The Naive Bayes classifier, which has strong assumptions of independence among features, is a common approach, whilst the class of trees is another less extreme example. In this thesis we propose the use of an information theory based technique as a mechanism for inference in Singly Connected Networks. We call this a Mutual Information Measure classifier, as it corresponds to the restricted class of trees built from mutual information. We show that the new approach provides for both an efficient and localised method of classification, with performance accuracies comparable with the less restricted general Bayesian networks. To improve the performance of the classifier, we additionally investigate the possibility of expanding the class Markov blanket by use of a Wrapper approach and further show that the performance can be improved by focusing on the class Markov blanket and that the improvement is not at the expense of increased complexity. Finally, the two methods are applied to the task of diagnosing the 'real' world medical domain, Acute Abdominal Pain. Known to be both a different and challenging domain to classify, the objective was to investigate the optiniality claims, in respect of the Naive Bayes classifier, that some researchers have argued, for classifying in this domain. Despite some loss of representation capabilities we show that the Mutual Information Measure classifier can be effectively applied to the domain and also provides a recognisable qualitative structure without violating 'real' world assertions. In respect of its 'selective' variant we further show that the improvement achieves a comparable predictive accuracy to the Naive Bayes classifier and that the Naive Bayes classifier's 'overall' performance is largely due the contribution of the majority group Non-Specific Abdominal Pain, a group of exclusion

Composting modelling : towards a better understanding of the fundamentals, applications for enhanced nutrient recycling, greenhouse gas reduction, and improved decision-making

Cette thèse de doctorat vise à consolider, développer et appliquer nos connaissances sur la modélisation du compostage, dans le but de fournir des informations, des outils et des perspectives accessibles et utilisables pour les chercheurs et les décideurs. L'espoir est que les travaux développés tout au long de cette thèse puissent aider à optimiser les procédés de compostage, notamment en réduisant les émissions de gaz à effet de serre (GES) et en améliorant le recyclage des nutriments. A ce titre, la thèse est divisée en trois phases : (1) la phase 1 est une consolidation et un développement des notions fondamentales de la modélisation du compostage, (2) suivie de la phase 2, où la modélisation de la perte de nutriments et des émissions de GES est étudiée, (3) avec la phase 3 qui est axée sur la manière d'assurer que ce travail puisse être utilisé par les décideurs et acteurs dans le milieu de compostage. Dans la première phase, une revue complète et systématique de l'ensemble de la littérature sur la modélisation du compostage a été entreprise (chapitre 2), cherchant à fournir une meilleure compréhension du travail qui a été fait et sur la direction des travaux futurs. Ceci a été suivi d'une étudie détaillée des approches de modélisation cinétique actuelles, notamment par rapport aux facteurs de corrections cinétiques appliqués à travers le domaine (chapitre 3). La phase 2 s'est ensuite focalisée sur les notions spécifiques relatives aux émissions de GES et aux pertes de nutriments lors du compostage, et à la modélisation de ces phénomènes. Cette thèse présente les réacteurs expérimentaux et le plan conçu pour suivre et évaluer le processus de compostage (chapitre 4), ainsi que le modèle de compostage compréhensif développé pour prédire avec précision les émissions et la transformation des nutriments pendant le compostage (chapitre 5). Enfin, la phase 3 visait à rendre ces informations facilement et largement utilisables. Cela a commencé par une évaluation des meilleures pratiques pour développer des modèles et des systèmes d'aide à la décision pour la prise de décision environnementale (chapitre 6), suivi par le développement de nouvelles approches de modélisation cinétique simples (chapitre 7), culminant avec le développement, l'ajustement paramétrique et la validation d'un modèle de compostage parcimonieux (chapitre 8). Grâce à ce travail, une base consolidée de l'état actuel de la modélisation du compostage a été développée, suivie par l'exploration et le développement de connaissances et d'outils à la fois fondamentaux et applicables.This PhD thesis aims consolidating, developing, and applying our knowledge on composting modelling, with the goal of providing accessible and usable information, tools, and perspectives for researchers and decision-makers alike. The hope is that the work developed throughout this dissertation can help in optimizing composting, notably by reducing greenhouse gas (GHG) emissions and improving nutrient recycling. As such, the thesis is divided into three phases: (1) phase 1 is a consolidation and development of the fundamentals of composting modelling, (2) followed by phase 2, where the modelling of nutrient loss and GHG emissions is investigated, (3) with phase 3 focusing on how to ensure that this work can be used by decision-makers. In the first phase, a comprehensive and systematic review of the entirety of the literature on composting modelling was undertaken (chapter 2), seeking to provide a better understanding on the work that has been done and guidance on where future work should focus and how it should be approached. This review then raised some interesting questions regarding modelling approaches, notably regarding modelling of composting kinetics, which was studied in detail through the evaluation of current modelling approaches (chapter 3). Phase 2 then focused on the specific notions relating to GHG emissions and nutrient loss during composting, and how to model these phenomena. This section starts with a presentation of the experimental reactors and plan designed to monitor and evaluate the composting process (chapter 4), followed by the comprehensive composting model developed to accurately predict emissions and nutrient transformation during composting (chapter 5). Finally, phase 3 aimed to make this information easily and widely usable, especially for decision-makers. This started with a review on the best practices to develop models and decision support systems for environmental decision-making (chapter 6), followed by the development of novel simple kinetic modelling approaches (chapter 7), culminating with the development, calibration, and validation of a parsimonious composting model (chapter 8). Through this work, a consolidated basis of the current state on composting modelling has been developed, followed-up by the exploration and development of both fundamental and applicable knowledge and tools

Desarrollo y evaluación de modelos para la toma de decisiones: caracterización de la producción de anguilas (Anguilla anguilla L.) en sistemas intensivos

El objetivo principal de la tesis desarrollada es conseguir una mejora del régimen de explotación en una piscifactoría de anguilas europeas (Anguilla anguilla L.) a través de unas herramientas de predicción y simulación lo suficientemente fiables, que permitan al acuicultor prever a corto y medio plazo los acontecimientos que van a suceder desde un punto de vista patológico, así como a nivel de control de parámetros físicos, químicos y biológicos. De manera global, los principales beneficios que se obtienen de la aplicación del sistema de predicción y simulación son: 1,- Desde el punto de vista patológico, se puede llevar a cabo un diagnóstico precoz de la enfermedad, gracias al cual será posible la aplicación del tratamiento preventivo adecuado, con el consecuente ahorro de productos terapéuticos y disminución de la mortalidad. Para ello, se ha programado un sistema experto en cuyo motor de inferencia se combina el potencial de la lógica borrosa con el método de transmisión de la incertidumbre conocido como teoría de Dempster-Shafer. 2,- La predicción de las condiciones físico-químicas permite al mismo tiempo evaluar el comportamiento de la planta en su totalidad, de tal forma que el gestor puede adecuar las actividades de los operarios para, en el caso de un peligro significativo, establecer un sistema de control que modifique la desviación de los parámetros afectados hasta sus niveles normales. El problema de la predicción de los parámetros físico-químicos se ha resuelto mediante la aplicación conjunta de técnicas estadísticas clásicas (regresión múltiple de series temporales, suavizados lineales, modelos MM, AR, ARMA y ARIMA) y modelos de Redes Neuronales Computacionales (RNCs). 3,- La predicción de la evolución de los parámetros poblacionales facilita el control de la producción, mortalidad, distribución de las raciones diarias de alimento, seguimiento del stock, y puede servir como indicativo de la estabilidad o variabilidad de las condiciones ambientales dentro de la explotación. Permite, por otra parte, la definición de lo que se conoce como estrategia de producción, en la que se define la forma de comercialización del producto y su impacto sobre el stoc

Diseño y gestión óptimos de sistemas de impulsión y de almacenamiento de agua para riego

Los sistemas que se estudiarán serán los constituidos por un conjunto de impulsores de motor eléctrico que tomará el agua de una fuente de suministro y del que partirá una conducción hasta depósito o balsa de regulación desde el/las cuales abastecerá la red de distribución según las demandas de agua existentes. El depósito o balsa, además de cumplir de acumulación del recurso agua, permitirá la reducción de costes energéticos al poder permitir una adaptación entre las horas de bombeo y el tipo de discriminación horaria de la facturación eléctrica. El objetivo principal es conseguir el régimen de explotación integral óptimo que origine el menor coste teniendo en cuenta la capacidad hidráulica de la estación de bombeo, el volumen del depósito o balsa, el coste de elevación del metro cúbico de agua y el contrato del suministro de energía eléctrica, todo ello compatibilizado con la capacidad de satisfacer una demanda dada. De esta manera, se planifica el esquema de operación de los sistemas de impulsión y de almacenamiento de agua en un futuro próximo, que en este trabajo es la campaña de riesgos, teniendo en cuenta las implicaciones en el diseño de los elementos de regulació